Conformal Prediction Intervals with Tail-Specific Guarantees

Battle Count: 71

Highly relevant for quantitative trading and risk management. The paper directly addresses Value-at-Risk estimation with distribution-free finite-sample guarantees, which is critical for regulatory compliance and capital allocation. The financial application demonstrates practical utility for ETF return forecasting with GARCH(1,1)-t models. The tail-specific calibration is particularly valuable for downside risk control in portfolio management, and the adaptive framework handles non-stationarity inherent in financial markets. The signed quantile score avoids the overcoverage issues of standard CQR under skewed returns.

Moderate complexity. The core split CP procedure is straightforward, but the full DtACI implementation requires managing multiple expert instances with different learning rates, exponential weighting, pinball loss evaluation, and separate calibration for each tail. The intersection construction adds a layer of complexity. Hyperparameter tuning (gamma grid, eta, sigma) requires careful selection. The paper provides Algorithm 1 pseudocode and detailed supplementary implementation notes, reducing implementation barriers.

The paper provides detailed algorithm pseudocode (Algorithm 1 for DtACI), specifies all hyperparameters (gamma grids, learning rates, exploration parameters), describes the data generating processes for simulations (N=3000, R=500 MC runs), and includes supplementary material with full proofs. Financial data uses publicly available ETFs (SPY, TQQQ, XLE). However, no explicit code repository is mentioned.